Hostname: page-component-848d4c4894-pjpqr Total loading time: 0 Render date: 2024-07-01T07:42:51.144Z Has data issue: false hasContentIssue false
  • English
  • Français

The description and classification of evolutionary mode: a computational approach

Published online by Cambridge University Press:  08 April 2016

Peter D. Roopnarine
Peter D. Roopnarine*
Affiliation:
Department of Invertebrate Zoology and Geology, California Academy of Sciences, Golden Gate Park, San Francisco, California 94118-4599. E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

The incorporation of the random walk model into stratophenetic analysis marked a turning point by presenting a potential null model for microevolutionary patterns. Random walks are derived from a family of statistical fractals, and their statistics can be reconstructed using appropriate techniques. This paper lays the foundation for the explicit and uniform description of evolutionary mode in stratophenetic series using random walk null models and the information contained within incompletely preserved time series.

The method relies upon the iterative analysis of subseries of an original stratophenetic series by measuring the presence of deviations from statistical randomness as the lineage evolves. This measure, and its probability of significance (evaluated using a randomization test), forms the dimensions of a descriptive space for microevolutionary modes. Each stratophenetic series can then be viewed as a journey through this space. Computer simulation of various evolutionary modes demonstrates that different modes, for example stasis and gradualism, have differing trajectories and occupy different regions of the microevolutionary space. The method is applied to two published foraminiferal stratophenetic series, the Mio-Pliocene Globorotalia plesiotumida-tumida punctuated transition and an anagenetic trend in the Late Cretaceous Contusotruncana fornicata-contusa lineage. An anagenetic trend is strongly supported in the latter example, whereas transformation of the Globorotalia species seems to result from the fluctuating effectiveness of constraining processes.

Type
Articles
Information
Paleobiology , Volume 27 , Issue 3 , Summer 2001 , pp. 446 - 465
Copyright
Copyright © The Paleontological Society 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Literature Cited

Anderson, P. W. 1994. The Eightfold Way to the theory of complexity: a prologue. Pp. 7–16in Cowan, G. A., Pines, D., and Meltzer, D., eds. Complexity: metaphors, models and reality. Volume XIX of Santa Fe Institute studies in the sciences of complexity. Addison Wesley, Reading, Mass.Google Scholar
Bookstein, F. L. 1987. Random walk and the existence of evolutionary rates. Paleobiology 13:446–464.Google Scholar
Bookstein, F. L. 1988. Random walk and the biometrics of morphological characters. Evolutionary Biology 9:369–398.Google Scholar
Bookstein, F. L. 1991. Morphometric tools for landmark data. Cambridge University Press, New York.Google Scholar
Charlesworth, B. 1984. Some quantitative methods for studying evolutionary patterns in single characters. Paleobiology 10:308–318.CrossRefGoogle Scholar
Clyde, W. C., and Gingerich, P. D. 1994. Rates of evolution in the dentition of early Eocene Cantius: comparison of size and shape. Paleobiology 20:506–522.Google Scholar
Darwin, C. 1859. The origin of species. Reprint, 1985, Penguin Books, London.Google Scholar
Dobzhansky, T. 1970. Genetics of the evolutionary process. Columbia University Press, New York.Google Scholar
Eaton, J. W. 1998. GNU Octave: a high-level interactive language for numerical applications. GNU/Free Software Foundation, Boston.Google Scholar
Eble, G. J. 1999. On the dual nature of chance in evolutionary biology and paleobiology. Paleobiology 25:75–87.Google Scholar
Efron, B., and Tibshirani, R. J. 1993. An introduction to the bootstrap. Monographs on Statistics and Applied Probability 57. Chapman and Hall, New York.Google Scholar
Eldredge, N. 1989. Macroevolutionary dynamics: species, niches, and adaptive peaks. McGraw-Hill, New York.Google Scholar
Eldredge, N., and Gould, S. J. 1972. Punctuated equilibria: an alternative to phyletic gradualism. Pp. 82–115in Schopf, T. J. M., ed. Models in paleobiology. Freeman, Cooper, San Francisco.Google Scholar
Erwin, D. H., and Anstey, R. L. 1995. Speciation in the fossil record. Pp. 11–38in Erwin, D. H. and Anstey, R. L., eds. New approaches to speciation in the fossil record. Columbia University Press, New York.Google Scholar
Feder, J. 1988. Fractals. Plenum, New York.Google Scholar
Felsenstein, J. 1988. Phylogenies and quantitative methods. Annual Review of Ecology and Systematics 19:445–471.Google Scholar
Gingerich, P. D. 1974. Stratigraphic record of Early Eocene Hyopsodus and the geometry of mammalian phylogeny. Nature 248:107–109.Google Scholar
Gingerich, P. D. 1993. Quantification and comparison of evolutionary rates. American Journal of Science 293A:453–478.Google Scholar
Gingerich, P. D. 1995. Fractal interpretation of trends in evolutionary time series. Geological Society of America Abstracts with Programs 27:A371.Google Scholar
Hall, B. K. 1992. Evolutionary developmental biology. Chapman and Hall, London.Google Scholar
Hurst, H. E. 1951. Long-term storage capacity of reservoirs. Transactions of the American Society of Civil Engineering 116:770–808.Google Scholar
Kauffman, E. G. 1977. Evolutionary Rates and Biostratigraphy. Pp. 109–141in Kauffman, E. G. and Hazel, J. E., eds. Concepts and methods of biostratigraphy. Dowden, Hutchinson and Ross. Stroudsburg, Penn.Google Scholar
Kowalewski, M., Goodfriend, G. A., and Flessa, K. W. 1998. High-resolution estimates of temporal mixing within shell beds: the evils and virtues of time-averaging. Paleobiology 24:287–304.Google Scholar
Kucera, M., and Malmgren, B. A. 1998. Differences between evolution of mean form and evolution of new morphotypes: an example from Late Cretaceous planktonic foraminifera. Paleobiology 24:49–63.Google Scholar
Lande, R. 1980. Microevolution in relation to macroevolution. Paleobiology 6:235–238.Google Scholar
Lande, R. 1986. The dynamics of peak shifts and the pattern of morphological evolution. Paleobiology 12:343–354.Google Scholar
MacLeod, N. 1991. Punctuated anagenesis and the importance of stratigraphy to paleobiology. Paleobiology 17:167–188.Google Scholar
Malmgren, B. A., Berggren, W., and Lohmann, G. P. 1983. Evidence for punctuated gradualism in the late Neogene Globorotalia tumida lineage of planktonic foraminifera. Paleobiology 9:377–389.Google Scholar
Mandelbrot, B. 1999. Multifractals and 1/f Noise. Springer, New York.Google Scholar
Mandelbrot, B., and Wallis, J. R. 1969. Some long-run properties of geophysical records. Water Resources Research 5:321–340.Google Scholar
Plotnick, R. E. 1986. A fractal model for the distribution of stratigraphic hiatuses. Journal of Geology 94:885–890.Google Scholar
Plotnick, R. E. 1995. Introduction to fractals. In Middleton, G. V., Plotnick, R. E., and Rubin, D. M., eds. Nonlinear dynamics and fractals: new numerical techniques for sedimentary data. SEPM Short Course 36:1–28.Google Scholar
Plotnick, R. E., and Prestegaard, K. 1993. Fractal analysis of geologic time series. Pp. 193–210in Lam, N. and DeCola, L., eds. Fractals in geography. Prentice-Hall, Englewood Cliffs, N.J.Google Scholar
Plotnick, R. E., and Prestegaard, K. 1995. Time series analysis I. In Middleton, G. V., Plotnick, R. E., and Rubin, D. M., eds. Nonlinear dynamics and fractals: new numerical techniques for sedimentary data. SEPM Short Course 36:47–67.Google Scholar
Raup, D. M. 1977. Stochastic models in evolutionary paleontology. Pp. 59–78in Hallam, A., ed. Patterns of evolution. Elsevier, Amsterdam.Google Scholar
Raup, D. M., and Crick, R. E. 1981. Evolution of single characters in the Jurassic ammonite Kosmoceras. Paleobiology 7:200–215.Google Scholar
Rieppel, O., and Grande, L. 1994. Summary and comments on systematic pattern and evolutionary process. Pp. 227–255in Grande, L. and Rieppel, O., eds. Interpreting the hierarchy of nature: from systematic patterns to evolutionary process theories. Academic Press, San Diego.Google Scholar
Roopnarine, P. D., Byars, G., and Fitzgerald, P. 1999. Anagenetic evolution, stratophenetic patterns, and random walk models. Paleobiology 25:41–57.Google Scholar
Schaefer, S. A., and Lauder, G. V. 1986. Historical transformation of functional design: evolutionary morphology of feeding mechanisms of loricarioid catfishes. Systematic Zoology 35:489–508.Google Scholar
Schroeder, M. 1991. Fractals, chaos, power laws. W. H. Freeman, New York.Google Scholar
Shaw, A. B. 1964. Time in stratigraphy. McGraw-Hill, New York.Google Scholar
Sheldon, P. R. 1996. Plus ça change—a model for stasis and evolution in different environments. Palaeogeography, Palaeoclimatology, Palaeoecology 127:209–227.Google Scholar
Slade, G. 1996. Random walks. American Scientist 84:146–153.Google Scholar
Springer, K. B., and Murphy, M. A. 1994. Punctuated stasis and collateral evolution in the Devonian lineage of Monograptus hercynicus. Lethaia 27:119–128.Google Scholar
Tshudy, D., Baumiller, T. K., and Sorhannus, U. 1998. Morphologic change in the clawed lobster Hoploparia (Nephropidae) from the Cretaceous of Antarctica. Paleobiology 24:64–738.Google Scholar
Wright, S. 1932. The roles of mutation, inbreeding, crossbreeding, and selection in evolution. Proceedings of the 11th International Congress of Genetics 1:356–366.Google Scholar